Troubleshooting Fine-Tuning Challenges Training Course
This advanced-level course equips participants with the knowledge and skills to troubleshoot common challenges in fine-tuning machine learning models. From addressing data imbalances to resolving overfitting and ensuring proper model convergence, participants will gain practical expertise to handle real-world issues in fine-tuning scenarios.
This instructor-led, live training (online or onsite) is designed for advanced-level professionals seeking to refine their skills in diagnosing and solving fine-tuning challenges for machine learning models.
By the end of this training, participants will be able to:
- Diagnose issues such as overfitting, underfitting, and data imbalance.
- Implement strategies to improve model convergence.
- Optimise fine-tuning pipelines for enhanced performance.
- Debug training processes using practical tools and techniques.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice opportunities.
- Hands-on implementation within a live-lab environment.
Course Customisation Options
- To request a customised training session for this course, please contact us to arrange.
Course Outline
Introduction to Fine-Tuning Challenges
- Overview of the fine-tuning process
- Common challenges in fine-tuning large models
- Understanding the impact of data quality and preprocessing
Addressing Data Imbalances
- Identifying and analysing data imbalances
- Techniques for handling imbalanced datasets
- Using data augmentation and synthetic data
Managing Overfitting and Underfitting
- Understanding overfitting and underfitting
- Regularisation techniques: L1, L2, and dropout
- Adjusting model complexity and training duration
Improving Model Convergence
- Diagnosing convergence problems
- Choosing the right learning rate and optimiser
- Implementing learning rate schedules and warm-ups
Debugging Fine-Tuning Pipelines
- Tools for monitoring training processes
- Logging and visualising model metrics
- Debugging and resolving runtime errors
Optimising Training Efficiency
- Batch size and gradient accumulation strategies
- Utilising mixed precision training
- Distributed training for large-scale models
Real-World Troubleshooting Case Studies
- Case study: Fine-tuning for sentiment analysis
- Case study: Resolving convergence issues in image classification
- Case study: Addressing overfitting in text summarisation
Summary and Next Steps
Requirements
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Understanding of machine learning concepts including training, validation, and evaluation
- Familiarity with fine-tuning pre-trained models
Audience
- Data scientists
- AI engineers
Open Training Courses require 5+ participants.
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